Data and ML Infrastructure Engineer

🔥 18 hours ago

⚓ Rhode Island – Remote

⚓ Rhode Island – Remote

💵 $150k - $185k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷 Infrastructure Engineer

👻 Ghost score 20%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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HavocAI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2024

📦 Logistics

🏭 Manufacturing

🎖️ Defense

💰 Seed Round on 2024-09

Logistics • Manufacturing • Defense

HavocAI is a developer of collaborative autonomy for maritime operations, offering a modular software and vehicle stack that enables fleets of autonomous maritime systems to perform contested logistics, sensor fusion and tracking, domain awareness, and escort-and-engage missions. Their product suite includes onboard autonomy (HAVOC OS), scalable communications (HAVOC CLOUD), and a handheld operator interface (HAVOC CONTROL), marketed as a single solution for theater-scaled security and rapid deployment. HavocAI emphasizes real-time, team-led autonomous solutions that run across diverse environments and supports both hardware (autonomous vessels) and software deployment.

📋 Description

• Build and maintain infrastructure for video, imagery, telemetry, sensor data, autonomy logs, mission data, and field-test data • Own data ingestion, storage, indexing, metadata, access patterns, and lifecycle management within HavocAI’s data lake • Develop scalable pipelines that transform raw operational data into curated datasets for ML training, evaluation, debugging, and analysis • Build tools for searching, filtering, tagging, and retrieving data across platforms, missions, operating conditions, and events • Design infrastructure capable of handling large volumes of multimodal operational data efficiently and reliably • Build workflows to select, clean, label, validate, and version datasets • Partner with Autonomy, Perception, Software, and Field Operations teams to identify high-value data for model development and system evaluation • Support annotation and labeling workflows for video, imagery, tracks, telemetry, and other ML inputs • Develop reproducible dataset-generation workflows for training, validation, regression testing, and benchmarking • Integrate datasets and data infrastructure with model training, experiment tracking, evaluation, and deployment workflows • Support multimodal dataset construction, including synchronization and alignment across sensors and data streams • Develop automated checks for missing streams, corrupted files, synchronization issues, metadata gaps, labeling errors, and pipeline failures • Establish standards for dataset quality, lineage, versioning, and reproducibility • Build monitoring and observability around critical data pipelines and infrastructure • Troubleshoot complex data and infrastructure issues and drive them through resolution • Use field data, logs, and test results to help engineering teams understand system performance and identify opportunities for improvement • Build self-service tools that make operational data easier for engineers to discover, access, analyze, and use • Partner closely with Autonomy, Perception, Software, Simulation, Field Operations, and Program teams • Translate engineering and ML requirements into scalable data capabilities • Improve workflows for replaying, visualizing, analyzing, and comparing operational data • Maintain clear documentation, data standards, and best practices for internal data use, governance, and security • Within the first 12 months, build reliable field-data pipelines, improve data discoverability, establish reproducible dataset workflows, improve quality and observability, and enable faster model improvement and deployment

🎯 Requirements

• Bachelor’s degree in Computer Science, Data Science, Machine Learning, Electrical Engineering, Computer Engineering, Robotics, Applied Mathematics, or a related technical field • 3+ years of experience in data engineering, ML infrastructure, data platforms, backend systems, MLOps, or related engineering roles • Experience designing and operating production data pipelines for large-scale structured, semi-structured, or unstructured datasets • Experience working with video, imagery, time-series telemetry, sensor data, logs, or other high-volume operational data • Strong programming skills in Python and SQL • Experience with cloud storage, object stores, data lakes, databases, distributed processing, or modern data platforms • Familiarity with dataset versioning, metadata management, data lineage, access controls, and reproducible data workflows • Strong software engineering fundamentals, including testing, reliability, maintainability, and observability • Strong debugging skills and comfort working across complex data pipelines and production infrastructure • Ability to operate independently and take ownership in a fast-moving engineering environment • U.S. citizenship and ability to obtain and maintain a U.S. Government security clearance • Nice to have: experience with ML infrastructure, MLOps, training pipelines, experiment tracking, model evaluation, or model registries • Nice to have: experience with S3-compatible storage, PostgreSQL, Spark, Ray, Airflow, Dagster, Kubernetes, Docker, or Kafka • Nice to have: experience with data catalogs, dataset versioning platforms, feature stores, or labeling tools • Nice to have: experience building search, replay, visualization, or analysis tools for video, telemetry, logs, or sensor data • Nice to have: experience supporting annotation workflows for computer vision, perception, tracking, or autonomy • Nice to have: familiarity with sensor synchronization, timestamp alignment, calibration metadata, log replay, or multimodal dataset construction • Nice to have: experience with security, access controls, auditability, and data-handling requirements in government or defense environments • Nice to have: experience supporting defense, robotics, autonomy, aerospace, or dual-use technology programs • Nice to have: active or prior security clearance

🏖️ Benefits

• 100% Employer paid Health, Dental and Vision Insurance for you and your families • Life Insurance (Employer Paid) • Ability to participate in the companies 401k program (Matching) • Unlimited PTO policy with an enforced 2 week minimum • Equity Package • Work / Home Office Stipend • Global Entry • 16 Week Paid Parental Leave • Monthly Health and Wellness Stipend • Bonus